The “BINternet” has as a focal point a smart collection method to optimize waste management practices. It suggests a digitalization and optimization of the dustbin management in order for cities to have better control over emissions and costs.
It is always a rarity to look beneath the surface and focus on the meaning hidden inside when we talk about the garbage, can we realize that what we are talking about is an energy, but which has been disposed wrongly. Naturally, a question follows – what should be the right way to tackle the waste? In this post, we will continuously pay attention to the smart integration into the existing waste management solutions, including collection, storage, and re-use techniques.
At a start of the smart waste management concept, this proposal will cover a new collection method, which will optimize collection routes for garbage trucks by digitalizing the real-time volumes of the dustbins. All these data will be kept and analyzed for both location and volume. This will help the garbage transportation center to create a reliable database, just like the Internet, all input data will be linked together for further analysis and results can be presented. We can call this dustbins database as “the BINternet”. In parallel, companies are also producing smart bin containers, that compact the waste and are run by solar energy. Those innovative smart bins can hold up to eight times the capacity of regular bins because of its compaction technology that is automatically run when waste come into the bin. Then, by wireless technology it transmits the information to the database.
To select the optimized route, there are two steps to be followed; the digitalization, and the optimization. In the following sections, we will discuss these two steps.
What we are expecting is the optimized route for waste collection. To programme such an optimization route selection system, we need to analyze incoming data; to get the data, we need to monitor dustbins; to monitor bins, we need to install special tracking sensors. This special sensor is the one who plays an important role in the digitalization process. Sensors can monitor fill level and other indicators such as temperature and tilt within waste containers. However, for different methods and different requirements, different types of data would be collected. Here we chose two types of data to be explained: the height of the bin and the location of the latitude and longitude of the bin.
As a usual practice for garbage collection, the garbage truck reaches to all bins inside his assigned area. However, it is usually the case that some dustbins located in relatively rural areas in the city are not even half-full when it is collected. In other words, the collecting frequency for the dustbins in the downtown and the rural area is quite different. Therefore, it would be more practical if the truck only went by the full bins and collect only those.
The data collected is a real-time data and it is called ER (empty ratio), monitored by a sensor installed on the bin.
We calculate the ER we have the formula below, where
If a bin’s ER is over 80% then this bin is being selected for the truck to collect it. When the driver is leaving for collection, the app installed in the truck will run automatically to select the optimized route, considering only the selected bins and the real-time traffic.
Besides the ER data, there is also other data that needs to be considered, the location. There are lots of methods to measure the concrete location, for instance, the latitude and longitude, and GIS-based applications. With GIS app and its shortest path application, we could easily locate each bin and draw them on a map.
To summarize, we locate the bins separated in the city and draw them on a 2D map, while based on real-time ER, we give each bin a monitored data. By doing so, we digitalize the dustbins data, build a database, and transfer a 3D to a 2D optimization problem.
After tracking data and building a database, we can start to analyze the optimized collection route. In order to do so, we can choose either Dynamic Programming (DP) or the Shortest Path Problem (SPP).
Dynamic programming, as a basic algorithm, is a technique which can be used to solve problems in which a sequence of decisions must be made over time. The objective here is to minimized miles within an expected time spending on the road. By programming such a DP code, we can get the result. This result could be used then as guidance for waste collection as showed in figure 1.
Figure 1: Example of a smart route based on DP code for waste collection
We can also use the SPP to analyze this situation and get a corresponded result. The SPP has been broadly used by many companies and apps, for instance, Mapquest and Google Maps. There is also a software being developed, called ArcGIS. If we could install this app on each truck, and negotiate with the software owner to make slight changes that will better suit the waste collection case, there will be a bright future for developing the smart waste collection in the city.
However, for this initiative, there are also challenges. The first one is how to maintain such a big real-time database. As it is common for a city to have millions of dustbins, even if just for a specific area, the number of bins is also large to manage. Also, what frequency should we keep to update the real-time data? To be more practical, should we separate the bin for glass, plastic, bottles, and bio-residuals? What should we do if there is heavy density inside the bin that might not be able to fully reflect the volume? Some of these questions can be answered by the accumulation of first-hand experiences, although some of the answers might be more tricky and to find out. Furthermore, which organization should be the regulator? Should it be the city or another party? Besides these main challenges, there are also other technical problems, as of how to update and upgrade the code and how to cooperate with the IT solution provider. These could generate a great opportunity, while also having chances of being the obstacle.
Having all that in mind, studying and addressing all the possibilities and challenges of waste management and also knowing that the waste management in Germany is one of the smartest in the world, InSell has been providing original and economically saving solutions by working together with leading technology innovators in Germany and Switzerland. Providing technological offerings assistance such as GIS, data analytics and applications for smart integration (sensors) for urban waste management, transportation, and processing in Germany locations, it has as its objectives the development of operational excellence in collection and monitoring for, later on, the waste to be turned into wealthier products. Additionally, the company also sustains consulting to devise collection strategies, treatment, segregation technologies and management of waste from municipal bodies, industrial setups, agricultural and hazardous types according to their special needs.
More specifically, InSell offers GIS consulting, system development and implementation. The main GIS services provided are:
- Customized GIS Software Development and Enterprise Integration Solutions
- Mobile GIS application solutions in connection with spatial information systems
- Web GIS development
- GIS integration with design, development, and maintenance of spatial databases
- Development of location-based apps
Furthermore, a full range of digital Photogrammetry post-processing services, LIDAR Data Processing and Mapping and UAV are provided such as:
- 3D city models in all resolutions (LOD1, LOD2 or LOD3).
- Recording and evaluation of infrastructure assets of roads (condition, surface, etc.).
The newest project from InSell in this field is advising the implementation of a project that produces energy and wealthy products from waste together with CNTY China. The idea is to implement the project first in regions of India.